Literature DB >> 34650333

Representativeness of the FluWatchers Participatory Disease Surveillance Program 2015-2016 to 2018-2019: How do participants compare with the Canadian population?

Mireille Desroches1, Liza Lee1, Shamir Mukhi2, Christina Bancej1.   

Abstract

BACKGROUND: FluWatch is Canada's national surveillance system that monitors the spread of influenza. Its syndromic surveillance component monitors the spread of influenza-like illness (ILI) in near-real time for signals of unusual or increased activity. Syndromic surveillance data are collected from two main sources: the Sentinel Practitioner ILI Reporting System and FluWatchers.We evaluated the representativeness of the most recent participant population to understand changes in representativeness since 2015, to identify demographic and geographic gaps and correlates/determinants of participation to characterize a typical participant.
METHODS: In this serial cross-sectional study, characteristics of participants during four consecutive influenza seasons (2015-2016, 2016-2017, 2017-2018 and 2018-2019) were compared with the 2016 Canadian Census and the 2015-2016, 2016-2017, 2017-2018 and 2018-2019 National Seasonal Influenza Vaccination Coverage Surveys. Associations between demographic factors and the level of user participation were also analyzed among the 2018-2019 FluWatchers population.
RESULTS: Infants (0-4 years) and older adults (65 years and older) were under-represented in FluWatchers across all four influenza seasons. Female and urban participants were significantly over-represented. Vaccination coverage remained significantly higher among the FluWatchers populations from the past four influenza seasons across all age groups. Level of participation among FluWatchers was associated with age and vaccination status, but not with sex or geography. Over its four years of implementation, the FluWatchers participant population became more representative of the Canadian population with respect to age and geography (urban/rural and provincial/territorial).
CONCLUSION: FluWatchers participants under-represent the tails of Canada's age distribution and over-represent those who engage in health promoting behaviours as indicated by high influenza vaccine coverage, consistent with typical volunteer-based survey response biases. Representativeness would likely improve with targeted recruitment of under-represented groups, such as males, older adults and Canadians living in rural areas.

Entities:  

Keywords:  : influenza-like illness; Canada; crowdsourcing; digital epidemiology; online disease monitoring; participatory surveillance; public health; respiratory illness; syndromic surveillance

Year:  2021        PMID: 34650333      PMCID: PMC8448176          DOI: 10.14745/ccdr.v47i09a03

Source DB:  PubMed          Journal:  Can Commun Dis Rep        ISSN: 1188-4169


  11 in total

1.  Syndromic Surveillance: is it a useful tool for local outbreak detection?

Authors:  Kirsty Hope; David N Durrheim; Edouard Tursan d'Espaignet; Craig Dalton
Journal:  J Epidemiol Community Health       Date:  2006-05       Impact factor: 3.710

2.  Flutracking weekly online community survey of influenza-like illness annual report, 2016

Authors:  Sandra J Carlson; Daniel Cassano; Michelle T Butler; David N Durrheim; Craig B Dalton
Journal:  Commun Dis Intell (2018)       Date:  2019-04-15

3.  Flutracking weekly online community survey of influenza-like illness annual report 2011 and 2012.

Authors:  Sandra J Carlson; Craig B Dalton; Michelle T Butler; John Fejsa; Elissa Elvidge; David N Durrheim
Journal:  Commun Dis Intell Q Rep       Date:  2013-12-31

4.  Determinants of follow-up participation in the Internet-based European influenza surveillance platform Influenzanet.

Authors:  Paolo Bajardi; Alessandro Vespignani; Sebastian Funk; Ken Td Eames; W John Edmunds; Clément Turbelin; Marion Debin; Vittoria Colizza; Ronald Smallenburg; Carl E Koppeschaar; Ana O Franco; Vitor Faustino; Annasara Carnahan; Moa Rehn; Daniela Paolotti
Journal:  J Med Internet Res       Date:  2014-03-10       Impact factor: 5.428

5.  Building influenza surveillance pyramids in near real time, Australia.

Authors:  Craig B Dalton; Sandra J Carlson; Michelle T Butler; Elissa Elvidge; David N Durrheim
Journal:  Emerg Infect Dis       Date:  2013-11       Impact factor: 6.883

6.  Sociodemographic and health-(care-)related characteristics of online health information seekers: a cross-sectional German study.

Authors:  Laura Nölke; Monika Mensing; Alexander Krämer; Claudia Hornberg
Journal:  BMC Public Health       Date:  2015-01-29       Impact factor: 3.295

7.  Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.

Authors:  Mauricio Santillana; André T Nguyen; Mark Dredze; Michael J Paul; Elaine O Nsoesie; John S Brownstein
Journal:  PLoS Comput Biol       Date:  2015-10-29       Impact factor: 4.475

8.  Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System.

Authors:  Kristin Baltrusaitis; Mauricio Santillana; Adam W Crawley; Rumi Chunara; Mark Smolinski; John S Brownstein
Journal:  JMIR Public Health Surveill       Date:  2017-04-07

9.  Evaluating the feasibility and participants' representativeness of an online nationwide surveillance system for influenza in France.

Authors:  Marion Debin; Clément Turbelin; Thierry Blanchon; Isabelle Bonmarin; Alessandra Falchi; Thomas Hanslik; Daniel Levy-Bruhl; Chiara Poletto; Vittoria Colizza
Journal:  PLoS One       Date:  2013-09-11       Impact factor: 3.240

10.  The representativeness of a European multi-center network for influenza-like-illness participatory surveillance.

Authors:  Pietro Cantarelli; Marion Debin; Clément Turbelin; Chiara Poletto; Thierry Blanchon; Alessandra Falchi; Thomas Hanslik; Isabelle Bonmarin; Daniel Levy-Bruhl; Alessandra Micheletti; Daniela Paolotti; Alessandro Vespignani; John Edmunds; Ken Eames; Ronald Smallenburg; Carl Koppeschaar; Ana O Franco; Vitor Faustino; AnnaSara Carnahan; Moa Rehn; Vittoria Colizza
Journal:  BMC Public Health       Date:  2014-09-20       Impact factor: 3.295

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